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  • Unger, Jörg F. (16) (remove)

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Damage detection of a prestressed concrete beam using modal strains (2005)
Unger, Jörg F. ; Teughels, A. ; De Roeck, G.
Damage detection of a prestressed concrete beam using modal strains
System identification and damage detection of a prestressed concrete beam (2006)
Unger, Jörg F. ; Teughels, A. ; De Roeck, G.
System identification and damage detection of a prestressed concrete beam
Adaptation of the natural element method for crack growth simulations (2004)
Unger, Jörg F. ; Most, Thomas ; Bucher, Christian ; Könke, Carsten
Adaptation of the natural element method for crack growth simulations
DISCRETE CRACK SIMULATION OF CONCRETE USING THE EXTENDED FINITE ELEMENTMETHOD (2006)
Unger, Jörg F. ; Könke, Carsten
The extended finite element method (XFEM) offers an elegant tool to model material discontinuities and cracks within a regular mesh, so that the element edges do not necessarily coincide with the discontinuities. This allows the modeling of propagating cracks without the requirement to adapt the mesh incrementally. Using a regular mesh offers the advantage, that simple refinement strategies based on the quadtree data structure can be used to refine the mesh in regions, that require a high mesh density. An additional benefit of the XFEM is, that the transmission of cohesive forces through a crack can be modeled in a straightforward way without introducing additional interface elements. Finally different criteria for the determination of the crack propagation angle are investigated and applied to numerical tests of cracked concrete specimens, which are compared with experimental results.
PARAMETER IDENTIFICATION OF MESOSCALE MODELS FROM MACROSCOPIC TESTS USING BAYESIAN NEURAL NETWORKS (2010)
Unger, Jörg F. ; Könke, Carsten
In this paper, a parameter identification procedure using Bayesian neural networks is proposed. Based on a training set of numerical simulations, where the material parameters are simulated in a predefined range using Latin Hypercube sampling, a Bayesian neural network, which has been extended to describe the noise of multiple outputs using a full covariance matrix, is trained to approximate the inverse relation from the experiment (displacements, forces etc.) to the material parameters. The method offers not only the possibility to determine the parameters itself, but also the accuracy of the estimate and the correlation between these parameters. As a result, a set of experiments can be designed to calibrate a numerical model.
Simulation of concrete using the extended finite element method (2006)
Unger, Jörg F. ; Könke, Carsten
Simulation of concrete using the extended finite element method
Coupling of scales in a multiscale simulation using neural networks (2008)
Unger, Jörg F. ; Könke, Carsten
Coupling of scales in a multiscale simulation using neural networks
Neural networks as material models within a multiscale approach (2007)
Unger, Jörg F. ; Könke, Carsten
Neural networks as material models within a multiscale approach
Numerical Models for the simulation of concrete on the mesoscale (2005)
Unger, Jörg F. ; Eckardt, Stefan ; Könke, Carsten
Numerical Models for the simulation of concrete on the mesoscale
Modelling of cohesive crack growth in concrete structures with the extended finite element method (2007)
Unger, Jörg F. ; Eckardt, Stefan ; Könke, Carsten
Modelling of cohesive crack growth in concrete structures with the extended finite element method
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